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Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users

Author

Listed:
  • Yunhong Xu

    (Kunming University of Science and Technology)

  • Dehu Yin

    (Kunming University of Science and Technology)

  • Duanning Zhou

    (Eastern Washington University)

Abstract

With the development of social interaction techniques and social tagging mechanisms, online academic community as a new platform has greatly changed the way users organize and share knowledge. The large amount of social tagging data occurred on online academic community provides us a channel to systematically understand users’ tagging behavior. Based on data collected from a specific online academic community, this research first classifies users into two categories: active and inactive users. After that, growth models (damped exponential model, normal model and fluctuating model) are employed to investigate tagging behavior for both active and inactive users. Factors that might influence the likelihood of the growth models are also identified based on multinomial logistic regression. This research expands our understanding on users’ tagging behavior and factors that may affect their tagging behavior in the context of online academic community.

Suggested Citation

  • Yunhong Xu & Dehu Yin & Duanning Zhou, 2019. "Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users," Information Systems Frontiers, Springer, vol. 21(4), pages 761-772, August.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:4:d:10.1007_s10796-018-9891-2
    DOI: 10.1007/s10796-018-9891-2
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    References listed on IDEAS

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    1. Xuwei Pan & Shenglan He & Xiyong Zhu & Qingmiao Fu, 2016. "How users employ various popular tags to annotate resources in social tagging: An empirical study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1121-1137, May.
    2. Zhang, Yin & Zhang, Bin & Gao, Kening & Guo, Pengwei & Sun, Daming, 2012. "Combining content and relation analysis for recommendation in social tagging systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5759-5768.
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    6. Youngok Choi & Sue Yeon Syn, 2016. "Characteristics of tagging behavior in digitized humanities online collections," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1089-1104, May.
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    8. repec:bla:jamist:v:60:y:2009:i:9:p:1741-1755 is not listed on IDEAS
    9. Jennifer Golbeck & Jes Koepfler & Beth Emmerling, 2011. "An experimental study of social tagging behavior and image content," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(9), pages 1750-1760, September.
    10. Yi-ling Lin & Christoph Trattner & Peter Brusilovsky & Daqing He, 2015. "The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(9), pages 1785-1798, September.
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    Cited by:

    1. Luvai Motiwalla & Amit V. Deokar & Surendra Sarnikar & Angelika Dimoka, 2019. "Leveraging Data Analytics for Behavioral Research," Information Systems Frontiers, Springer, vol. 21(4), pages 735-742, August.

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